Abstract:
This study provides an advanced deep learning method for predicting liver cirrhosis using
a Kaggle dataset of 615 entries with a target attribute "Liver Cirrhosis Status" that classifies
answers as 'Yes' (75 cases) or 'No' (540 cases). Complete Data collection, Preprocessing,
Model selection, Training, and Evaluation are all part of the suggested methodology. The
trial findings reveal that the Artificial Neural Network (ANN) outperforms other
categorization algorithms with its high accuracy of 98.01%. This discusses the model's
extraordinary ability to identify detailed patterns in the medical dataset, proving its
potential for accurate liver cirrhosis prediction. The ANN's achievement shows the utility
of advanced deep learning techniques in medical testing, particularly for complex problems
such as liver cirrhosis prediction. The group methods RandomForestClassifier and
AdaBoosting, as well as SVC's unfair capabilities, all performed well, suggesting they are
suitable for capturing subtle relationships within the dataset. These findings add to the
growing body of knowledge about the use of complex neural networks created in
healthcare, paving the way for better patient outcomes through early and exact prediction
of liver cirrhosis. The findings of the study have important implications for continuing
efforts to improve medical diagnostic capacities through the use of modern artificial
intelligence technologies.
